Filtern
Dokumenttyp
Sprache
- Englisch (2)
Volltext vorhanden
- ja (2)
Gehört zur Bibliographie
- nein (2)
Schlagworte
- ARDS (1)
- COVID-19 (1)
- Cardiomyocyte signaling pathways (1)
- Cardioprotection (1)
- Cardiovascular disease (1)
- Comorbidities (1)
- Critical care (1)
- Drug targeting (1)
- Endothelial permeability (1)
- Extracellular RNA (eRNA) (1)
Institut
- Medizin (2)
In this meeting report, particularly addressing the topic of protection of the cardiovascular system from ischemia/reperfusion injury, highlights are presented that relate to conditioning strategies of the heart with respect to molecular mechanisms and outcome in patients’ cohorts, the influence of co-morbidities and medications, as well as the contribution of innate immune reactions in cardioprotection. Moreover, developmental or systems biology approaches bear great potential in systematically uncovering unexpected components involved in ischemia–reperfusion injury or heart regeneration. Based on the characterization of particular platelet integrins, mitochondrial redox-linked proteins, or lipid-diol compounds in cardiovascular diseases, their targeting by newly developed theranostics and technologies opens new avenues for diagnosis and therapy of myocardial infarction to improve the patients’ outcome.
Background: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. Methods: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. Results: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict “survival”. Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients’ age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. Conclusions: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration “ClinicalTrials” (clinicaltrials.gov) under NCT04455451.